11_revisting_early_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In notebooks 01-06, we looked at forecasts and actuals for 2021-04-09 thorugh 2021-04-12 for several examples. We intentionally left that date range our of our regression training sets, and not let's revisit them with our probabilistic tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "from quantile_forest import RandomForestQuantileRegressor\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in data and load models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('data_files/full_2021_2023_dataset.pkl')\n",
    "booster = xgb.Booster()\n",
    "booster.load_model(\"regression_models/xgboost_model.json\")\n",
    "qrf = joblib.load('regression_models/qrf_file.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = df.dropna()\n",
    "approx_nameplate = df.p_ac_all_cs.max() # use max of clear sky power as proxy for nameplate rating\n",
    "filtered_df = filtered_df[filtered_df['p_ac_all_cs'] >= approx_nameplate*.001]\n",
    "pi_list_xgb = [.9995, .999, .99, .96, .57] \n",
    "pi_list_rf = [.995, .99, .98, .95, .5]\n",
    "\n",
    "X=filtered_df[['tcc_std']].copy()\n",
    "X['max_min_norm'] = filtered_df['max_min_norm']\n",
    "X['csi_fcast'] = filtered_df['csi_fcast']\n",
    "X['zen'] = filtered_df['zen']\n",
    "X['az'] = filtered_df['az']\n",
    "X['month'] = filtered_df['month']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the XGBoost model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X[(X.index >= '2021-04-09') & (X.index <= '2021-04-13')]\n",
    "\n",
    "# predict\n",
    "y_pred = booster.inplace_predict(X_test)\n",
    "\n",
    "# create percentile list:\n",
    "pi_list = pi_list_xgb\n",
    "percentile_list = [(1-x)/2 for x in pi_list] + [.5] + [1-(1-x)/2 for x in reversed(pi_list)]\n",
    "percentile_list = [round(elem, 6) for elem in percentile_list]\n",
    "num_intervals = len(pi_list)\n",
    "\n",
    "# create a results dataframe\n",
    "result_df = X_test.copy()\n",
    "\n",
    "# merge orginal DF, marking duplicate columns and then dropping them\n",
    "result_df = result_df.merge(df, left_index=True, right_index=True, suffixes=('', '_drop'))\n",
    "result_df.drop(result_df.filter(regex='_drop$').columns, axis=1, inplace=True)\n",
    "\n",
    "# adding CS-normalized quantiles/percentiles, and then un-normalizing them to get power\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_csi']=y_pred[:,i]\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_p_ac']=y_pred[:,i]*result_df['p_ac_all_cs']\n",
    "\n",
    "# fill in overnight \n",
    "result_df = result_df.reset_index().set_index('time_center_labeled').asfreq('1h')\n",
    "\n",
    "# save results into a new dataframe\n",
    "result_df_XGB = result_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the Random Forest model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\willh\\miniforge3\\envs\\pvlib_herbie_env3\\lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestQuantileRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# predict\n",
    "y_pred = qrf.predict(X_test, quantiles=percentile_list)\n",
    "\n",
    "# create percentile list:\n",
    "pi_list = pi_list_rf\n",
    "percentile_list = [(1-x)/2 for x in pi_list] + [.5] + [1-(1-x)/2 for x in reversed(pi_list)]\n",
    "percentile_list = [round(elem, 6) for elem in percentile_list]\n",
    "num_intervals = len(pi_list)\n",
    "\n",
    "# create a results dataframe\n",
    "result_df = X_test.copy()\n",
    "\n",
    "# merge orginal DF, marking duplicate columns and then dropping them\n",
    "result_df = result_df.merge(df, left_index=True, right_index=True, suffixes=('', '_drop'))\n",
    "result_df.drop(result_df.filter(regex='_drop$').columns, axis=1, inplace=True)\n",
    "\n",
    "# adding CS-normalized quantiles/percentiles, and then un-normalizing them to get power\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_csi']=y_pred[:,i]\n",
    "for i in range(len(percentile_list)):\n",
    "    result_df[str(percentile_list[i])+'_p_ac']=y_pred[:,i]*result_df['p_ac_all_cs']\n",
    "\n",
    "# fill in overnight \n",
    "result_df = result_df.reset_index().set_index('time_center_labeled').asfreq('1h')\n",
    "\n",
    "# save results into a new dataframe\n",
    "result_df_RF = result_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the hybrid output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extreme lower bound from rf 0.0025_p_ac\n",
      "lower bounds 0.005_p_ac 0.0005_p_ac\n",
      "lower bounds 0.01_p_ac 0.005_p_ac\n",
      "lower bounds 0.025_p_ac 0.02_p_ac\n",
      "lower bounds 0.25_p_ac 0.215_p_ac\n",
      "mid 0.5_p_ac 0.5_p_ac\n",
      "upper bounds 0.75_p_ac 0.785_p_ac\n",
      "upper bounds 0.975_p_ac 0.98_p_ac\n",
      "upper bounds 0.99_p_ac 0.995_p_ac\n",
      "upper bounds 0.995_p_ac 0.9995_p_ac\n",
      "extreme upper bound from rf 0.9975_p_ac\n"
     ]
    }
   ],
   "source": [
    "# create new hyrbid dataframe, starting with RF because PIs were accurate so we can keep column names\n",
    "result_df_hybrid = result_df_RF.copy()\n",
    "\n",
    "# find columns that end in `_p_ac` (e.g., '0.015_p_ac')\n",
    "rf_columns = result_df_RF.filter(regex='_p_ac$').columns\n",
    "xgb_columns = result_df_XGB.filter(regex='_p_ac$').columns\n",
    "\n",
    "# loop through, selecting max of lower bounds and min of upper bounds\n",
    "for i in range(len(rf_columns)):\n",
    "    if i == 0: # first\n",
    "        print('extreme lower bound from rf', rf_columns[i])\n",
    "        result_df_hybrid[rf_columns[i]] = result_df_RF[rf_columns[i]]\n",
    "    elif i == len(rf_columns)-1: # last\n",
    "        print('extreme upper bound from rf', rf_columns[i])\n",
    "        result_df_hybrid[rf_columns[i]] = result_df_RF[rf_columns[i]]\n",
    "    elif i < (len(rf_columns)-1)/2:\n",
    "        print('lower bounds', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid[rf_columns[i]] = np.maximum(result_df_XGB[xgb_columns[i]], result_df_RF[rf_columns[i]])\n",
    "        # result_df_hybrid_2023[rf_columns[i]] = pd.DataFrame([result_df_XGB_2023[xgb_columns[i]], result_df_RF_2023[rf_columns[i]]]).max(axis=0)\n",
    "    elif i == (len(rf_columns)-1)/2:\n",
    "        print('mid', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid[rf_columns[i]] = result_df_RF[rf_columns[i]]\n",
    "    else:\n",
    "        print('upper bounds', rf_columns[i], xgb_columns[i])\n",
    "        result_df_hybrid[rf_columns[i]] = np.minimum(result_df_XGB[xgb_columns[i]], result_df_RF[rf_columns[i]])\n",
    "\n",
    "\n",
    "result_df = result_df_hybrid.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(num_intervals):\n",
    "    alph=0.1+(i/10.)\n",
    "    plt.fill_between(result_df.index,# x\n",
    "                     result_df[str(percentile_list[i])+'_p_ac'].fillna(0), # lower\n",
    "                     result_df[str(percentile_list[len(percentile_list)-i-1])+'_p_ac'].fillna(0), # upper\n",
    "                     color='blue',\n",
    "                     alpha=alph,\n",
    "                     step='mid',\n",
    "                     label=(str(pi_list[i]*100)+'%'))\n",
    "    \n",
    "plt.plot(result_df.index,\n",
    "         result_df[str(percentile_list[int((len(percentile_list)-1)/2)])+'_p_ac'],\n",
    "         label='Hybrid expected',color='brown', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index,result_df['p_ac_all_act'],label='actual',color='black', drawstyle='steps-mid')\n",
    "plt.plot(result_df.index,result_df['p_ac_all_fcast'],label='DA fcast (HRRR coarse)',color='orange', drawstyle='steps-mid')\n",
    "plt.ylabel('AC Power (MW)')\n",
    "# plt.ylim([0,4500])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And save the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df.to_pickle('data_files/hybrid_prob_fcast_results_20210408.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our next notebook, let's compare the 2023 results from our forecast with a reference persistence ensemble: [12_compare_with_persistence_ensemble.ipynb](12_compare_with_persistence_ensemble.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}